






"""simple docstring"""
import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


snake_case : Optional[Any]     		=		1_6
snake_case : Optional[Any]     		=		3_2


def 							A						(				__snake_case: Accelerator ,     __snake_case: int = 1_6 ,     __snake_case: str = "bert-base-cased"  )    ->		Any:

       """simple docstring"""





       __magic_name__      				=    AutoTokenizer.from_pretrained(__snake_case  )
       __magic_name__      				=    load_dataset('glue' ,     'mrpc'  )

       def tokenize_function(__snake_case: Union[str, Any]  ):
              # max_length=None => use the model max length (it's actually the default)
              __magic_name__      				=    tokenizer(examples['sentence1'] ,     examples['sentence2'] ,     truncation=__snake_case ,     max_length=__snake_case  )
              return outputs

       # Apply the method we just defined to all the examples in all the splits of the dataset
       __magic_name__      				=    datasets.map(
           __snake_case ,     batched=__snake_case ,     remove_columns=['idx', 'sentence1', 'sentence2'] ,     load_from_cache_file=__snake_case  )

       # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
       # transformers library
       __magic_name__      				=    tokenized_datasets.rename_column('label' ,     'labels'  )

       def collate_fn(__snake_case: Any  ):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              if accelerator.distributed_type == DistributedType.TPU:
                     return tokenizer.pad(__snake_case ,     padding='max_length' ,     max_length=1_2_8 ,     return_tensors='pt'  )
              return tokenizer.pad(__snake_case ,     padding='longest' ,     return_tensors='pt'  )

       # Instantiate dataloaders.
       __magic_name__      				=    DataLoader(
           tokenized_datasets['train'] ,     shuffle=__snake_case ,     collate_fn=__snake_case ,     batch_size=__snake_case  )
       __magic_name__      				=    DataLoader(
           tokenized_datasets['validation'] ,     shuffle=__snake_case ,     collate_fn=__snake_case ,     batch_size=__snake_case  )

       return train_dataloader, eval_dataloader


def 							A						(				__snake_case: Optional[Any] ,     __snake_case: Optional[Any]  )    ->		int:

       """simple docstring"""





       __magic_name__      				=    Accelerator()

       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       __magic_name__      				=    config['lr']
       __magic_name__      				=    int(config['num_epochs']  )
       __magic_name__      				=    int(config['seed']  )
       __magic_name__      				=    int(config['batch_size']  )
       __magic_name__      				=    args.model_name_or_path

       set_seed(__snake_case  )
       __magic_name__				,  __magic_name__      				=    get_dataloaders(__snake_case ,     __snake_case ,     __snake_case  )

       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       __magic_name__      				=    AutoModelForSequenceClassification.from_pretrained(__snake_case ,     return_dict=__snake_case  )

       # Instantiate optimizer
       __magic_name__      				=    (
           AdamW
           if accelerator.state.deepspeed_plugin is None
           or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
           else DummyOptim
       )
       __magic_name__      				=    optimizer_cls(params=model.parameters() ,     lr=__snake_case  )

       if accelerator.state.deepspeed_plugin is not None:
              __magic_name__      				=    accelerator.state.deepspeed_plugin.deepspeed_config[
                  'gradient_accumulation_steps'
              ]
       else:
              __magic_name__      				=    1
       __magic_name__      				=    (len(__snake_case  ) * num_epochs) // gradient_accumulation_steps

       # Instantiate scheduler
       if (
           accelerator.state.deepspeed_plugin is None
           or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
       ):
              __magic_name__      				=    get_linear_schedule_with_warmup(
                  optimizer=__snake_case ,     num_warmup_steps=0 ,     num_training_steps=__snake_case ,     )
       else:
              __magic_name__      				=    DummyScheduler(__snake_case ,     total_num_steps=__snake_case ,     warmup_num_steps=0  )

       # Prepare everything
       # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
       # prepare method.
       __magic_name__				,  __magic_name__				,  __magic_name__				,  __magic_name__				,  __magic_name__      				=    accelerator.prepare(
           __snake_case ,     __snake_case ,     __snake_case ,     __snake_case ,     __snake_case  )

       # We need to keep track of how many total steps we have iterated over
       __magic_name__      				=    0
       # We also need to keep track of the stating epoch so files are named properly
       __magic_name__      				=    0

       # Now we train the model
       __magic_name__      				=    evaluate.load('glue' ,     'mrpc'  )
       __magic_name__      				=    0
       __magic_name__      				=    {}
       for epoch in range(__snake_case ,     __snake_case  ):
              model.train()
              for step, batch in enumerate(__snake_case  ):
                     __magic_name__      				=    model(**__snake_case  )
                     __magic_name__      				=    outputs.loss
                     __magic_name__      				=    loss / gradient_accumulation_steps
                     accelerator.backward(__snake_case  )
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

                     overall_step += 1

              model.eval()
              __magic_name__      				=    0
              for step, batch in enumerate(__snake_case  ):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device  )
                     with torch.no_grad():
                            __magic_name__      				=    model(**__snake_case  )
                     __magic_name__      				=    outputs.logits.argmax(dim=-1  )
                     # It is slightly faster to call this once, than multiple times
                     __magic_name__				,  __magic_name__      				=    accelerator.gather(
                         (predictions, batch['labels'])  )  # If we are in a multiprocess environment, the last batch has duplicates
                     if accelerator.use_distributed:
                            if step == len(__snake_case  ) - 1:
                                   __magic_name__      				=    predictions[: len(eval_dataloader.dataset  ) - samples_seen]
                                   __magic_name__      				=    references[: len(eval_dataloader.dataset  ) - samples_seen]
                            else:
                                   samples_seen += references.shape[0]
                     metric.add_batch(
                         predictions=__snake_case ,     references=__snake_case ,     )

              __magic_name__      				=    metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(F"""epoch {epoch}:""" ,     __snake_case  )
              __magic_name__      				=    eval_metric['accuracy']

              if best_performance < eval_metric["accuracy"]:
                     __magic_name__      				=    eval_metric['accuracy']

       if args.performance_lower_bound is not None:
              assert (
                  args.performance_lower_bound <= best_performance
              ), F"""Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"""

       accelerator.wait_for_everyone()
       if accelerator.is_main_process:
              with open(os.path.join(args.output_dir ,     'all_results.json'  ) ,     'w'  ) as f:
                     json.dump(__snake_case ,     __snake_case  )


def 							A						(				)    ->		Optional[Any]:

       """simple docstring"""





       __magic_name__      				=    argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'  )
       parser.add_argument(
           '--model_name_or_path' ,     type=__snake_case ,     default='bert-base-cased' ,     help='Path to pretrained model or model identifier from huggingface.co/models.' ,     required=__snake_case ,     )
       parser.add_argument(
           '--output_dir' ,     type=__snake_case ,     default='.' ,     help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.' ,     )
       parser.add_argument(
           '--performance_lower_bound' ,     type=__snake_case ,     default=__snake_case ,     help='Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.' ,     )
       parser.add_argument(
           '--num_epochs' ,     type=__snake_case ,     default=3 ,     help='Number of train epochs.' ,     )
       __magic_name__      				=    parser.parse_args()
       __magic_name__      				=    {'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 4_2, 'batch_size': 1_6}
       training_function(__snake_case ,     __snake_case  )


if __name__ == "__main__":
 main()







"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_big_bird import BigBirdTokenizer
else:
 snake_case : Tuple     		=		None

snake_case : Tuple     		=		logging.get_logger(__name__)
snake_case : List[Any]     		=		{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

snake_case : Dict     		=		{
    """vocab_file""": {
        """google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model""",
        """google/bigbird-roberta-large""": (
            """https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"""
        ),
        """google/bigbird-base-trivia-itc""": (
            """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"""
        ),
    },
    """tokenizer_file""": {
        """google/bigbird-roberta-base""": (
            """https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json"""
        ),
        """google/bigbird-roberta-large""": (
            """https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json"""
        ),
        """google/bigbird-base-trivia-itc""": (
            """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json"""
        ),
    },
}

snake_case : int     		=		{
    """google/bigbird-roberta-base""": 4_0_9_6,
    """google/bigbird-roberta-large""": 4_0_9_6,
    """google/bigbird-base-trivia-itc""": 4_0_9_6,
}


snake_case : List[str]     		=		"""▁"""



class        UpperCamelCase__ (				a_):







    """simple docstring"""
    __UpperCAmelCase           = VOCAB_FILES_NAMES
    __UpperCAmelCase           = PRETRAINED_VOCAB_FILES_MAP
    __UpperCAmelCase           = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __UpperCAmelCase           = BigBirdTokenizer
    __UpperCAmelCase           = ["""input_ids""", """attention_mask"""]
    __UpperCAmelCase           = []







    def __init__(		self :	Optional[int]				,    UpperCamelCase_ :	int=None				,    UpperCamelCase_ :	Dict=None				,    UpperCamelCase_ :	int="<unk>"				,    UpperCamelCase_ :	Tuple="<s>"				,    UpperCamelCase_ :	int="</s>"				,    UpperCamelCase_ :	Union[str, Any]="<pad>"				,    UpperCamelCase_ :	Optional[Any]="[SEP]"				,    UpperCamelCase_ :	int="[MASK]"				,    UpperCamelCase_ :	Tuple="[CLS]"				,    **UpperCamelCase_ :	Optional[Any]				,    ):





           '''simple docstring'''

           __magic_name__      				=    AddedToken(UpperCamelCase_				,    lstrip=UpperCamelCase_				,    rstrip=UpperCamelCase_ ) if isinstance(UpperCamelCase_				,    UpperCamelCase_ ) else bos_token
           __magic_name__      				=    AddedToken(UpperCamelCase_				,    lstrip=UpperCamelCase_				,    rstrip=UpperCamelCase_ ) if isinstance(UpperCamelCase_				,    UpperCamelCase_ ) else eos_token
           __magic_name__      				=    AddedToken(UpperCamelCase_				,    lstrip=UpperCamelCase_				,    rstrip=UpperCamelCase_ ) if isinstance(UpperCamelCase_				,    UpperCamelCase_ ) else unk_token
           __magic_name__      				=    AddedToken(UpperCamelCase_				,    lstrip=UpperCamelCase_				,    rstrip=UpperCamelCase_ ) if isinstance(UpperCamelCase_				,    UpperCamelCase_ ) else pad_token
           __magic_name__      				=    AddedToken(UpperCamelCase_				,    lstrip=UpperCamelCase_				,    rstrip=UpperCamelCase_ ) if isinstance(UpperCamelCase_				,    UpperCamelCase_ ) else cls_token
           __magic_name__      				=    AddedToken(UpperCamelCase_				,    lstrip=UpperCamelCase_				,    rstrip=UpperCamelCase_ ) if isinstance(UpperCamelCase_				,    UpperCamelCase_ ) else sep_token

           # Mask token behave like a normal word, i.e. include the space before it
           __magic_name__      				=    AddedToken(UpperCamelCase_				,    lstrip=UpperCamelCase_				,    rstrip=UpperCamelCase_ ) if isinstance(UpperCamelCase_				,    UpperCamelCase_ ) else mask_token

           super().__init__(
               UpperCamelCase_				,    tokenizer_file=UpperCamelCase_				,    bos_token=UpperCamelCase_				,    eos_token=UpperCamelCase_				,    unk_token=UpperCamelCase_				,    sep_token=UpperCamelCase_				,    pad_token=UpperCamelCase_				,    cls_token=UpperCamelCase_				,    mask_token=UpperCamelCase_				,    **UpperCamelCase_				,    )

           __magic_name__      				=    vocab_file
           __magic_name__      				=    False if not self.vocab_file else True







    def 				a__			(		self :	Union[str, Any]				,    UpperCamelCase_ :	List[int]				,    UpperCamelCase_ :	Optional[List[int]] = None ):





           '''simple docstring'''

           __magic_name__      				=    [self.sep_token_id]
           __magic_name__      				=    [self.cls_token_id]
           if token_ids_a is None:
                  return cls + token_ids_a + sep
           return cls + token_ids_a + sep + token_ids_a + sep







    def 				a__			(		self :	Optional[Any]				,    UpperCamelCase_ :	List[int]				,    UpperCamelCase_ :	Optional[List[int]] = None				,    UpperCamelCase_ :	bool = False ):





           '''simple docstring'''

           if already_has_special_tokens:
                  if token_ids_a is not None:
                         raise ValueError(
                             'You should not supply a second sequence if the provided sequence of '
                             'ids is already formatted with special tokens for the model.' )
                  return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

           if token_ids_a is None:
                  return [1] + ([0] * len(UpperCamelCase_ )) + [1]
           return [1] + ([0] * len(UpperCamelCase_ )) + [1] + ([0] * len(UpperCamelCase_ )) + [1]







    def 				a__			(		self :	Optional[Any]				,    UpperCamelCase_ :	List[int]				,    UpperCamelCase_ :	Optional[List[int]] = None ):





           '''simple docstring'''

           __magic_name__      				=    [self.sep_token_id]
           __magic_name__      				=    [self.cls_token_id]

           if token_ids_a is None:
                  return len(cls + token_ids_a + sep ) * [0]
           return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]







    def 				a__			(		self :	Union[str, Any]				,    UpperCamelCase_ :	str				,    UpperCamelCase_ :	Optional[str] = None ):





           '''simple docstring'''

           if not self.can_save_slow_tokenizer:
                  raise ValueError(
                      'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
                      'tokenizer.' )

           if not os.path.isdir(UpperCamelCase_ ):
                  logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
                  return
           __magic_name__      				=    os.path.join(
               UpperCamelCase_				,    (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )

           if os.path.abspath(self.vocab_file ) != os.path.abspath(UpperCamelCase_ ):
                  copyfile(self.vocab_file				,    UpperCamelCase_ )

           return (out_vocab_file,)